Generación de un índice de calidad de servicio para establecimientos turísticos de alimentos y bebidas
Bibliographic record
Abstract
La calidad representa un constante desafío que debe ser afrontado por las empresas tanto turísticas como no turísticas, debido a que ésta depende de la evolución de las necesidades que el cliente posee, por lo cual dentro de la literatura académica se han desarrollado diversos métodos o mecanismos para su medición. La presente investigación tiene como objetivo generar un índice que permita evaluar la calidad del servicio de alimentos y bebidas que brindan los establecimientos turísticos de la ciudad de Riobamba, provincia de Chimborazo. La metodología empleada corresponde a un análisis factorial que sintetiza la información de 10 criterios identificados en las tres plataformas virtuales de reserva más representativas del mundo. El principal aporte que brinda este estudio es la generación de un método de medición mucho más flexible que puede adaptarse a la variabilidad de la calidad dentro de los servicios turísticos, y que puede ser replicado hacia otros sectores.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".